notes on numbers and other randomness

Paradigm shift: From BI to MI

I listened to a Gartner webinar Information 2020: Uncertainty Drives Opportunity given by Frank Buytendijk yesterday and it got me thinking about the evolution (/revolution?) from business intelligence (BI) to machine intelligence (MI). I see this happening but not as fast as I’d like, as jaded as I am about BI. Buytendijk gave me some ideas for understanding this transformation.

From his book Dealing with Dilemmas, here’s Buytendijk’s formulation of S curves that show the uptake of new technologies and approaches over time, and how they are then replaced by newer technologies and approaches.

From the book:

A trend starts hopefully; with a lot of passion, a small group of people pioneer a technology, test a new business model, or bring a new product to market. This is usually followed by a phase of disappointment. The new development turns out to be something less than a miracle. Reality kicks in. At some point, best practices emerge and a phase of evolution follows. Product functionality improves, market adoption grows, and the profitability increases. Then something else is introduced, usually by someone else. … This replacement then goes through the same steps.

This is where I think we are with machine intelligence for enterprise software. We’ve reached the end of the line for business intelligence, the prior generation of analytics. It has plateaued. There’s not much more it can do to impact business outcomes–a topic that deserves its own post.

What instead? What next? Machine intelligence. MI not BI. Let’s let computers do what they do well–dispassionately crunch numbers. And let humans do what they do well–add context and ongoing insight and the flexibility that enterprise reality demands. Then weave these together into enterprise software applications that feature embedded, pervasive advanced analytics that optimize business micro-decisions and micro-actions continuously.

We’re not quite ready for that yet. While B2C data science has advanced, B2B data science has hardly launched, outside of some predictive modeling of leads in CRM and a bit of HR analytics. BI for B2B doesn’t give us the value we need. But MI for B2B has barely reached toddlerhood.

We are, in Buytendijk’s terms, in the “eye of ambiguity,” that space where one paradigm is plateauing but another has not yet proved itself. It’s very difficult at this point to jump from one S curve to the next–see how far apart they are?–because the new paradigm has not proven itself yet.

Recently one of the newish data scientists in my group said, “it seems like a lot of people don’t believe in this.” This, meaning data science. I agreed with him that it had yet to prove its worth in enterprise software and that many people did not believe it ever would. But it seems clear to me that sometime–in five years? ten years?–machines will help humans run enterprise processes much more efficiently and effectively than we are running them now.

My colleague’s comment reminded me of some points Peter Sheahan of ChangeLabs made at the Colorado Technology Association’s APEX conference last November. He proposed that we don’t have to predict the future in order to capitalize on future trends because people are already talking about what’s coming. Instead, we need to release ourselves from legacy biases and practices. This was echoed by Buytendijk in his webinar: “best practices are the solutions for yesterday’s problems.”

It’s exciting to be in on the acceleration at the front of the S curve but frustrating sometimes too. It’s hard to communicate that data science and the machine intelligence it can generate are not the same as business intelligence and data storytelling. People don’t get it. Then a few do. And a few more.